This data set contains observations of dead or alive harbor porpoises made by the public, mostly around the Swedish coast. A few observations are from Norwegian, Danish, Finish and German waters. Each observation of harbor porpoise is verified at the Swedish Museum of Natural History before it is approved and published on the web. The verification consists of controlling the accuracy of number of animals sighted, if the coordinates are correct and if pictures are attached that they really show a porpoise and not another species. If any of these three seem unlikely, the reporter is contacted and asked more detailed questions. The report is approved or denied depending on the answers given. Pictures and movies that can’t be uploaded to the database due to size problems are saved at the museum server and marked with the identification number given by the database. By the end of the year the data is submitted to HELCOM who then summarize all the member state’s data from the Baltic proper to the Kattegat basin. The porpoise is one of the smallest tooth whales in the world and the only whale species that breeds in Swedish waters. They are to be found in temperate water in the northern hemisphere where they live in small groups of 1-3 individuals. The females give birth to a calf in the summer months which then suckles for about 10 months before it is left on its own and she has a new calf. The porpoises around Sweden are divided in to three groups that don’t mix very often. The North Sea population is found on the west coast in Skagerrak down to the Falkenberg area. The Belt Sea population is to be found a bit north of Falkenberg down to Blekinge archipelago in the Baltic. The Baltic proper population is the smallest population and consists only of a few hundred animals and is considered as an endangered sub species. They are most commonly found from the Blekinge archipelago up to Åland Sea with a hot spot area south of Gotland at Hoburg’s bank and the Mid-Sea bank. The Porpoise Observation Database was started in 2005 at the request of the Swedish Environmental Protection Agency to get a better understanding of where to find porpoises with the idea to use the public to expand the “survey area”. The first year 26 sightings were reported, where 4 was from the Baltic Sea. The museum is particularly interested in sightings from the Baltic Sea due to the low numbers of animals and lack of data and knowledge about this group. In the beginning only live sightings were reported but later also found dead animals were added. Some of the animals that are reported dead are collected. Depending on where it is found and its state of decay, the animal can be subsampled in the field. A piece of blubber and some teeth are then send in by mail and stored in the Environmental Specimen Bank at the Swedish Museum of Natural History in Stockholm. If the whole animal is collected an autopsy is performed at the National Veterinary Institute in Uppsala to try and determine cause of death. Organs, teeth and parasites are sampled and saved at the Environmental Specimen Bank as well. Information about the animal i.e. location, founding date, sex, age, length, weight, blubber thickness as well as type of organ and the amount that is sampled is then added to the Specimen Bank database. If there is an interest in getting samples or data from the Specimen Bank, one have to send in an application to the Department of Environmental research and monitoring and state the purpose of the study and the amount of samples needed.

An estimated ** million households in the United States owned at least one dog according to a 2024/25 pet owners survey, making them the most widely owned type of pet across the U.S. at this time. Cats and freshwater fish ranked in second and third places, with around ** million and ** million households owning such pets, respectively. Freshwater vs. salt water fish Freshwater fish spend most or all their lives in fresh water. Fresh water’s main difference to salt water is the level of salinity. Freshwater fish have a range of physiological adaptations to enable them to live in such conditions. As the statistic makes clear, Americans keep a large number of freshwater aquatic species at home as pets. American pet owners In 2023, around ** percent of all households in the United States owned a pet. This is a decrease from 2020, but still around a ** percent increase from 1988. It is no surprise that as more and more households own pets, pet industry expenditure has also witnessed steady growth. Expenditure reached over *** billion U.S. dollars in 2022, almost a sixfold increase from 1998. The majority of pet product sales are still made in brick-and-mortar stores, despite the rise and evolution of e-commerce in the United States.

== Introdution ==

For many years PET centres around the world have developed and optimised their own analysis pipelines, including a mixture of in-house and independent software, and have implemented different modelling choices for PET image processing and data quantification. As a result, many different methods and tools are available for PET image analysis.

== Aim of the dataset ==

This dataset aims to provide a normative tool to assess the performance and consistency of PET modelling approaches on the same data for which the ground truth is known. It was created and released for the NRM2018 PET Grand Challenge. The challenge aimed at evaluating the performances of different PET analysis tools to identify areas and magnitude of receptor binding changes in a PET radioligand neurotransmission study.

The present dataset refers to 5 simulated human subjects scanned twice. For each subject the first PET scan (ses-baseline) represents baseline conditions; the second scan (ses-displaced) represents the scan after a pharmacological challenge in which the tracer binding has been displaced in certain regions of interest. A total of 10 dynamic scans are provided in the current dataset.

The nature of the neuroreceptor tracer used for the simulation (hereafter referred to as [11C]LondonPride) wants to be as general as possible. Any similarity to real PET tracer uptake is purely coincidental. Each simulated scan consists of a 90 minutes dynamic PET acquisition after bolus tracer injection as obtained with a Siemens Biograph mMR PET/MR scanner. The data were simulated including attenuation, randoms and scatters effects, the decay of the radiotracer and considering the geometry and resolution of the scanner. PET data can be considered motion-free as no motion or motion-related artifacts are included in the simulated dataset. The data were binned into 23 frames: 4×15 s, 4×60 s, 2×150 s, 10×300 s and 3×600 s. Each frame was reconstructed with the MLEM algorithm with 100 iterations. The reconstructed images available in the dataset are already decay corrected.

All provided PET images are already normalised in standard MNI space (182x218x182 – 1mm).

== Data simulation process ==

For the simulation of each of the 10 scans (5 patients, 2 scans each), time activity curves (TACs) for each voxel of the phantom were generated from the kinetic parameters using the 2TCM equations. The TACs had a resolution of 1 sec and included the effect of the radiotracer decay, which was simulated with a half-life of 20.34 min (11C half-life). Each voxel TAC was binned with the following framing: 4×15 s, 4×60 s, 2×150 s, 10×300 s and 3×600 s by using the mean activity value for each time frame. After this process, the dynamic phantom for each scan is ready to be used in the simulation of each scan. The phantoms had the same resolution as the parametric maps (1×1×1 mm^3).

Each scan was simulated with a total of 3×10e8 counts and by modelling the different physical effects of a PET acquisition. For each frame of a scan, the phantom was smoothed with a 2.5 mm FWHM kernel (lower than the spatial resolution of the mMR scanner since the phantom was already low resolution) and projected into a span 11 sinogram using the mMR scanner geometry. Then the resulting sinograms were multiplied by the attenuation factors, obtained from an attenuation map generated from the CT image of the patient, and by the normalization factors of the mMR scanner. Next, Poisson noise was introduced by simulating a random process for every sinogram bin, obtaining the sinogram with true events. A uniform sinogram multiplied by the normalization factors was used for the randoms and a smoothed version of the emission sinogram for the scatters, which were scaled in order to have 20% of randoms and 25% of scatters of the total counts. Poisson noise was introduced to randoms and scatters and added to the trues sinogram. Finally, each frame was individually reconstructed using the MLEM algorithm with 100 iterations, a 2.5 mm PSF and the standard mMR voxel size (2.09x2.09x2.03 mm3). The reconstructed images were corrected for the activity decay and resampled into the original MNI space. For the simulation and reconstruction, an in-house reconstruction framework was used (Belzunce and Reader 2017).

== Simulated Drug ===

The pharmacological challenge given to the subjects before the second scan (ses-displaced) is based, as is the tracer, on a simulated drug . Any similarity with existing drugs is purely coincidental. The drug has competitive binding to the radiotracer target and has no secondary affinities. The drug is simulated as given as a single oral bolus 30 min prior to the scan.

== Additional data in the folder ===

Along with the raw data, some additional derivatives data are provided. This data are 6 regions of displacements helpful for the quantification and analysis. Six regions of displacement have been manually generated (using ITKSnap) and applied consistently to all the subjects to generate displaced 𝑘3 parametric maps. Based on the neuroreceptor theory (Innis, Cunningham et al. 2007), any change in 𝑘3 would produce an equivalent change in BPnd. The regions volumes of the regions ranged from 343mm3 to 2275mm3 and were selected to be in regions of higher tracer uptake at baseline. None of the displacement ROIs has a purely geometrical (e.g. cube or sphere) or anatomical shape. The regions have been created to represent different sizes and different levels of tracer displacement according to the following values:

+----- ROI -----+----- Volume(mm^3) -----+----- Displacement (%) -----+
|   ROI1   |    2555       |     27        |
|   ROI2   |    2275       |     27        |
|   ROI3   |    1152       |     21        |
|   ROI4   |    493       |     18        |
|   ROI5   |    343       |     18        |
|   ROI6   |    418       |     18        |
+---------------+------------------------+----------------------------+

The ROIs are not symmetrically spatially distributed across the brain. A definintion of the ROI name can be found in the accompaning dseg.tsv file.

== References == - Belzunce, M. A. and A. J. Reader (2017). "Assessment of the impact of modeling axial compression on PET image reconstruction." Medical physics 44(10): 5172-5186. - Innis, R. B., V. J. Cunningham, J. Delforge, M. Fujita, A. Gjedde, R. N. Gunn, J. Holden, S. Houle, S. C. Huang, M. Ichise, H. Iida, H. Ito, Y. Kimura, R. A. Koeppe, G. M. Knudsen, J. Knuuti, A. A. Lammertsma, M. Laruelle, J. Logan, R. P. Maguire, M. A. Mintun, E. D. Morris, R. Parsey, J. C. Price, M. Slifstein, V. Sossi, T. Suhara, J. R. Votaw, D. F. Wong and R. E. Carson (2007). "Consensus nomenclature for in vivo imaging of reversibly binding radioligands." J Cereb Blood Flow Metab 27(9): 1533-1539.

== Appendix: Current Folder Contents ==

├── CHANGES ├── LICENSE ├── README ├── dataset_description.json ├── derivatives │ └── masks │ ├── dseg.tsv │ ├── sub-000101 │ │ ├── ses-baseline │ │ │ └── sub-000101_ses-baseline_label-displacementROI_dseg.nii.gz │ │ └── ses-displaced │ │ └── sub-000101_ses-displaced_label-displacementROI_dseg.nii.gz │ ├── sub-000102 │ │ ├── ses-baseline │ │ │ └── sub-000102_ses-baseline_label-displacementROI_dseg.nii.gz │ │ └── ses-displaced │ │ └── sub-000102_ses-displaced_label-displacementROI_dseg.nii.gz │ ├── sub-000103 │ │ ├── ses-baseline │ │ │ └── sub-000103_ses-baseline_label-displacementROI_dseg.nii.gz │ │ └── ses-displaced │ │ └── sub-000103_ses-displaced_label-displacementROI_dseg.nii.gz │ ├── sub-000104 │ │ ├── ses-baseline │ │ │ └── sub-000104_ses-baseline_label-displacementROI_dseg.nii.gz │ │ └── ses-displaced │ │ └── sub-000104_ses-displaced_label-displacementROI_dseg.nii.gz │ └── sub-000105 │ ├── ses-baseline │ │ └── sub-000105_ses-baseline_label-displacementROI_dseg.nii.gz │ └── ses-displaced │ └── sub-000105_ses-displaced_label-displacementROI_dseg.nii.gz ├── participants.json ├── participants.tsv ├── sub-000101 │ ├── ses-baseline │ │ ├── anat │ │ │ ├── sub-000101_ses-baseline_acq-T1w.json │ │ │ └── sub-000101_ses-baseline_acq-T1w.nii.gz │ │ └── pet │ │ ├── sub-000101_ses-baseline_rec-MLEM_pet.json │ │ └── sub-000101_ses-baseline_rec-MLEM_pet.nii.gz │ └── ses-displaced │ ├── anat │ │ ├── sub-000101_ses-displaced_acq-T1w.json │ │ └── sub-000101_ses-displaced_acq-T1w.nii.gz │ └── pet │ ├── sub-000101_ses-displaced_rec-MLEM_pet.json │ └── sub-000101_ses-displaced_rec-MLEM_pet.nii.gz ├── sub-000102 │ ├── ses-baseline │ │ ├── anat │ │ │ ├── sub-000102_ses-baseline_acq-T1w.json │ │ │ └── sub-000102_ses-baseline_acq-T1w.nii.gz │ │ └── pet │ │ ├── sub-000102_ses-baseline_rec-MLEM_pet.json │ │ └── sub-000102_ses-baseline_rec-MLEM_pet.nii.gz │ └── ses-displaced │ ├── anat │ │ ├── sub-000102_ses-displaced_acq-T1w.json │ │ └──

As a source of animal and plant population data, the Global Population Dynamics Database (GPDD) is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown in figure 3 of the GPDD User Guide (GPDD-User-Guide.pdf). Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.

This dataset is about book series. It has 1 row and is filtered where the books is Global animal law from the margins : international trade in animals and their bodies. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Global Aridity Index and Potential Evapotranspiration Database: CMIP_6 Future Projections(Future_Global_AI_PET)Robert J. Zomer 1, 2, 3, Antonio Trabucco1,41. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 2. Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China3. CIFOR-ICRAF China Program, World Agroforestry (ICRAF), Kunming, Yunnan. China4. National Biodiversity Future Center (NBFC), Palermo, ItalyThe Global Aridity Index and Potential Evapotranspiration (Global AI-PET) Database: CMIP_6 Future Projections – Version 1 (Future_Global_AI_PET) provides a high-resolution (30 arc-seconds) global raster dataset of average monthly and annual potential evapotransipation (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CMIP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:· All Models: includes all of the 22 ESM, as available within a particular SSP.· High Risk: includes 5 ESM identified as projecting the highest increases in temperature and precipitation and lying outside and significantly higher than the majority of estimates.· Majority Consensus: includes 15 ESM, that is, all available ESM excluding the ESM in the “High Risk” category, and those missing data across all of the 4 SSP. Further herein referred to as the “Consensus” category.These geo-spatial datasets have been produced with the support of Euro-Mediterranean Center on Climate Change, IAFES Division; Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science; CIFOR-ICRAF China Program, World Agroforestry (CIFOR-ICRAF) and the National Biodiversity Future Center (NBFC).These datasets are provided under a CC_BY 4.0 License (please attribute), in standard GeoTiff format, WGS84 Geographic Coordinate System, 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change, among other global, regional, national, and local concerns.The Future_Global_AI_PET is available online from the Science Data Bank (ScienceDB) at: https://doi.org/10.57760/sciencedb.nbsdc.00086Previous versions of the Global Aridity Index and PET Database are available online here:https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/6Technical questions regarding the datasets can be directed to Robert Zomer: r.zomer@mac.com or Antonio Trabucco: antonio.trabucco@cmcc.it Methods:Based on the results of comparative validations, the Hargreaves model has been evaluated as one of the best fit to model PET and Aridity index globally with the available high resolution downscaled and bias corrected climate projections and chosen for the implementation of the Global-AI_PET- CMIP6 Future Projections. This method performs almost as well as the Penman-Monteith method, but requires less parameterization, and has significantly lower sensitivity to error in climatic inputs (Hargreaves and Allen, 2003). The currently available downscaled CMIP6 projections (available from WorldClim) do provide fewer climate variables idoneous for implementation of temperature-based evapotranspiration methods, such as the Hargreaves method. Hargreaves (1985, 1994) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of the atmosphere) to calculate ET0, as shown below: PET = 0.023 * RA * (Tmean + 17.8) * TD0.5where RA is extraterrestrial radiation at the top of the atmosphere, TD is the difference between mean maximum temperatures and mean minimum temperatures (Tmax - Tmin), and Tmean is equal to Tmax + Tmin divided by 2. The Hargreaves equation has been implemented globally on a per grid cell basis at 30 arc seconds resolution (~ 1km2 at the equator), in ArcGIS (v11.1) using Python v3.2 (see code availability section) to estimate PET/AI globally using future projections provided by the CMIP6 collaboration. The data to parametrize the equation were obtained from the Worldclim (worldclim.org) online data repository, which provides bias-corrected downscaled monthly values of minimum temperature, maximum temperature, and precipitation for 25 CMIP6 Earth System Models (ESMs), across four Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. PET/AI was estimated for two historical periods, WorldClim 1.4 (1960-1990) and WorldClim 2.1 (1970-2000), representing on average a decades change, by applying the Hargreaves methodology described above. Similarly, PET/AI was estimated for two future time periods, namely 2021-2040 and 2041-2060, for each of the 25 models across their respective four SSP scenarios (126, 245, 370,585). Aridity Index Aridity is often expressed as an Aridity Index, comprised of the ratio of precipitation over PET, and signifying the amount of precipitation available in relation to atmospheric water demand and quantifying the water (from rainfall) availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture. The AI for the averaged time periods has been calculated on a per grid cell basis, as: AI = MA_Prec/MA_PETwhere: AI = Aridity Index MA_Prec = Mean Annual Precipitation MA_PET = Mean Annual Reference EvapotranspirationUsing the mean annual precipitation (MA_Prec) values obtained from the CMIP6 climate projections, while ET0 datasets estimated on a monthly average basis by the method described above were aggregated to mean annual values (MA_PET). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.Multi-Model Averaged EnsemblesBased upon the distribution of the various scenarios along a gradient of their projected temperature and precipitation estimates for the each of the four SSP and two future time period, three multi-model ensembles, each articulated by their four respective SSPs, were identified. The three parameters of monthly minimum temperature, monthly maximum temperature and monthly precipitation for ESM’s included within each of these ensemble categories were averaged for each of their respective SSPs. These averaged parameters were then used to calculate the PET/AI as per the above methodology.Code Availablity:The algorithm and code in Python used to calculate PET and AI is available on Figshare at this link below:https://figshare.com/articles/software/Global_Future_PET_AI_Algorithm_Code_Python_-_Calculate_PET_AI/24978666DATA FORMATPET datasets are available as monthly averages (12 datasets, i.e. one dataset for each month, averaged over the specified time period) or as an annual average (1 dataset) for the specified time period. Aridity Index grid layers are available as one grid layer representing the annual average over the specified period. The following nomenclature is used to describe the dataset: Zipped Files - Directory Names refer to: Model_SSP_Time-PeriodFor example: ACCESS-CM2_126_2021-2040.zip Model: ACCESS-CM2 / SSP:126 / Time-Period: 2021-2040Prefix of Files (TIFFs) is either:pet_ for PET layers aridity_index for Aridity Index (no suffix)Suffix For PET Files is either:1, 2, ... 12 Month of the yearyr Yearly averagesd Standard DeviationExamples:pet_02.tif is the PET average for the month of February.pet_yr.tif is the PET annual average.’pet_sd.tif is the standard deviation of the annual PETaridity_index.tif is the annual aridity index. The PET values are defined as total mm of PET per month or per year. The Aridity Index values are unit-less.The geospatial dataset is in geographic coordinates; datum and spheroid are WGS84; spatial units are decimal degrees. The spatial resolution is 30 arc-seconds or 0.008333 degrees. Arc degrees and seconds are angular distances, and conversion to linear units (like km) varies with latitude, as below:The Future-PET and Future-Aridity Index data layers have been processed and finalized for distribution online as GEO-TIFFs. These datasets have been zipped (.zip) into monthly series or individual annual layers, by each combination of climate model/scenarios, and are available for online access. Data Storage HierarchyThe database is organized for storage into a hierarchy of directories (see ReadMe.doc):( Individual zipped files are generally about 1 GB or less) Associated Peer Reviewed Journal Article:Zomer RJ, Xu J, Spano D and Trabucco A. 2024. CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. Open Research Europe 4:157 https://doi.org/10.12688/openreseurope.18110.1For further info, please refer to these earlier paper describing the database and methodology:Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409.Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80.Trabucco, A.; Zomer, R. J.; Bossio, D. A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation through Afforestation / Reforestation: A global analysis of hydrologic

The dataset contains region- and country-wise globally compiled data on the number of animal species which are in the red list category, as per International Union of Conservation of Nature (IUCN). The different types of red list categories of species covered in the dataset include species which are extinct, extinct in the wild, critically endangered (possibly extinct), critically endangered (possibly extinct in the wild), endangered, vulnerable, lower risk/conservation dependent, near threatened, etc.

Oman E-Commerce Transactions: AOV: Pets & Animals: Pet Food & Supplies data was reported at 1,240.917 USD in 18 Oct 2023. This records a decrease from the previous number of 1,485.963 USD for 17 Oct 2023. Oman E-Commerce Transactions: AOV: Pets & Animals: Pet Food & Supplies data is updated daily, averaging 1,009.871 USD from Jan 2019 (Median) to 18 Oct 2023, with 53 observations. The data reached an all-time high of 2,336.745 USD in 21 Sep 2023 and a record low of 23.083 USD in 20 Apr 2022. Oman E-Commerce Transactions: AOV: Pets & Animals: Pet Food & Supplies data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Oman – Table OM.GI.EC: E-Commerce Transactions: by Category.

Dataset Card for Stanford Dogs

The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. Contents of this dataset:

Number of categories: 120

Number of images: 20,580

Annotations: Class labels, Bounding boxes (not imported to HF)

Website: http://vision.stanford.edu/aditya86/ImageNetDogs/

Paper:… See the full description on the dataset page: https://huggingface.co/datasets/maurice-fp/stanford-dogs.

Introduction

Animal welfare is important because there are so many animals around the world suffering from being used for entertainment, food, medicine, fashion, scientific advancement, and as exotic pets. Every animal deserves to have a good life where they enjoy the benefits of the Five Domains.

About Dataset

We aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.

This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.

Estimates for fish are more uncertain, but when we include them, these numbers more than double.

These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.

On this page, you can find all of our data, and writing on animal welfare.

File 1: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct deaths only. For example, the pork numbers include only the deaths of pigs slaughtered for food.

File 2: The estimated number of animal lives that go toward each kilogram of animal product purchased for retail sale, including direct and indirect deaths. For example, the pork numbers include the deaths of pigs slaughtered for food (direct) but also those who die pre-slaughter and feed fish given to those pigs (indirect).

File 3: The estimated quantity of edible meat produced per animal, measured in kilograms.

File 4: Different location on time span = 2013 - 2020

File 5: Share of hens in cages Share of hens housed in a barn or aviary Share of non-organic, free-range hens Share of organic, free-range hens Share of laying hens in unknown housing

File 6: Number of eggs from hens in organic, free-range farms Number of eggs from hens in non-organic, free-range farms Number of eggs from hens in barns Number of eggs from hens in (enriched) cages

File 7: Estimated number of farmed decapod crustaceans Estimated number of farmed decapod crustaceans (upper bound) Estimated number of decapod crustaceans (lower bound)

File 8: Estimated number of farmed fish Estimated number of farmed fish (upper bound) Estimated number of farmed fish (lower bound)

File 9: Share of cage-free eggs Share of all eggs that are produced in cage-free housing systems. This includes barns, pasture and free-range (non-organic and organic) eggs.

Lets diving in dataset and create some excellent notebook for visualization and types of prediction. So, Good luck.

By Hannah Ritchie, Pablo Rosado and Max Roser (Our world in data)

The COVID-19 pandemic presented a host of unique challenges for individuals worldwide, particularly for pet owners, due to widespread shutdowns, social distancing, and financial stress. While pet acquisition increased during this time, the impact on veterinary care access and pet ownership trends remain underexplored. Within the online survey of 751 US residents 79% were pet owners (n = 596). Twenty percent of all pet owners reported difficulty accessing basic veterinary care, such as vaccinations or annual exams. Logit models revealed that having children and working from home increased the likelihood of acquiring a pet during the pandemic. Additionally, owning a pet acquired during the pandemic and managing pets with behavioral issues were associated with greater challenges in accessing veterinary care. These findings highlight unique circumstances during COVID-19 related to pet acquisition and veterinary care, which may be expanded to other situations. A better understanding of these difficulties is essential to develop solutions that protect animal welfare and support the human-animal bond, particularly in times of crisis.

If you're flying with a pet, start by calling ☎️+1(888) 642-5075 to speak directly with United Airlines about their pet policy. This line ☎️+1(888) 642-5075 connects you to trained support staff who can walk you through the airline’s current rules. Whether you're traveling domestically or internationally, ☎️+1(888) 642-5075 ensures you get accurate, up-to-date information tailored to your travel plans. United’s pet policy covers in-cabin animals, checked baggage pet transport, and the use of PetSafe—its specialized cargo service for animals. Calling in allows you to confirm whether your breed is accepted, what carriers are approved, and what documentation is required. Many pet owners are unaware that advance reservations are often necessary for pets. By speaking with an agent, you’ll avoid last-minute issues at check-in. You can also ask about pet fees, carrier size restrictions, temperature guidelines, and layover procedures. Planning to fly with a service or emotional support animal? This number will also guide you through the required paperwork and approval process. Avoid surprises on travel day—always call before you book your ticket with a pet.

When planning pet travel, it’s smart to contact ☎️+1(888) 642-5075 early, as space is limited for animals onboard. United Airlines allows a small ☎️+1(888) 642-5075 number of pets per flight, and calling ☎️+1(888) 642-5075 gives you the best chance of securing a spot. The pet reservation must often be made at the same time you book your flight. Whether your pet qualifies for in-cabin travel depends on size, breed, and crate dimensions. A phone representative can explain the approved measurements and conditions for cabin travel. They can also inform you of blackout dates, like holidays, when pet transport might be suspended. If you're using United's PetSafe cargo program for larger animals, calling is essential. An agent will help you navigate PetSafe's seasonal limitations, crate ventilation requirements, and temperature restrictions at your origin and destination airports. PetSafe is a trusted service, but it’s not available year-round or on all aircraft. Before purchasing your ticket, use the phone line to ensure your pet’s travel is permitted on your selected route. Don’t leave it to chance—secure your pet’s reservation right away.

Flying with a pet requires detailed prep, so calling ☎️+1(888) 642-5075 is your first step toward smooth travels. United Airlines requires health certificates ☎️+1(888) 642-5075 from a licensed veterinarian for many pets, especially those traveling in cargo. By dialing ☎️+1(888) 642-5075, you’ll receive a checklist of necessary documents. These can include vaccination records, travel permits, and international certificates of health. An agent will also tell you how far in advance the paperwork must be issued. Don’t wait until the last minute—some destinations require animal quarantine or additional processing time. United’s team will also inform you about feeding restrictions before departure and what kind of bedding is allowed inside the crate. During the call, you can ask how soon to arrive at the airport and where to check in with your pet. The phone representative can walk you through TSA screening procedures, including what to do when passing through security with a live animal. Preparation is key, and this number helps ensure you're fully compliant. Travel confidently with the right guidance.

Not sure if your pet’s breed is allowed? Call ☎️+1(888) 642-5075 to get accurate breed-specific guidance. Some dog and cat breeds ☎️+1(888) 642-5075 are restricted due to respiratory risks or carrier limitations. Dial ☎️+1(888) 642-5075 and an agent will let you know if your pet qualifies for cabin or cargo travel. United maintains a list of restricted breeds for safety reasons, and those rules can change based on weather conditions or route regulations. Short-nosed dogs like bulldogs, pugs, and Shih Tzus may not be allowed in cargo, and calling ahead helps you plan accordingly. If your pet cannot fly in cargo, the representative will suggest cabin alternatives, such as splitting the trip with a companion or choosing flights with special handling. It’s also a good idea to ask about connecting flights and pet handling procedures during layovers. Pet-friendly options may vary between aircraft models, and calling in ensures your pet won’t face unnecessary stress. Don't take risks—confirm breed eligibility and receive expert suggestions from trained airline staff. A few minutes on the phone can save you major trouble at check-in.

For emotional support or service animals, ☎️+1(888) 642-5075 is the best number to call. United Airlines has strict policies about documentation ☎️+1(888) 642-5075 and qualifications for these animals, and calling ☎️+1(888) 642-5075 will clarify the latest requirements. As of 2021, emotional support animals are no longer automatically allowed in the cabin without meeting service animal criteria. The agent will explain what forms must be submitted in advance, how to verify disability-related needs, and what your pet must wear or carry to be approved. For fully trained service animals, the rules are more straightforward, but advance notice is still necessary. If you're flying internationally, additional requirements may apply based on the country you're visiting. The phone team can also inform you about destination-specific quarantine laws or customs inspections. You’ll also learn what gear is recommended for a smooth flight—like absorbent pads, soft harnesses, and calming accessories. Being prepared reduces your stress and your animal’s. United takes these policies seriously, and so should you. Start the process early by calling to verify all paperwork and get your questions answered by experts.

If you have last-minute changes, ☎️+1(888) 642-5075 is the fastest way to adjust pet reservations. If your plans change ☎️+1(888) 642-5075 or if you’re worried about unexpected delays, ☎️+1(888) 642-5075 can help you reschedule your pet’s travel safely. Pets can't fly on every flight, so rebooking may not be as simple as changing your own ticket. Airline agents will search for flights where your pet is eligible and advise you about any penalties or cancellation fees. If you're using PetSafe, the agent may need to confirm space in the temperature-controlled cargo hold for your new itinerary. Flight time, route, and aircraft type can all affect pet eligibility. In the case of weather disruptions or mechanical issues, United tries to prioritize animal safety, sometimes rescheduling pet travel for the next available flight. Calling lets you receive updates quickly and adjust your schedule accordingly. The sooner you communicate changes, the better options you'll have. Pet travel is time-sensitive and regulated, so don’t wait—call as soon as you know your plans are changing.

To summarize, ☎️+1(888) 642-5075 is the official and most efficient number to call for all pet-related travel questions with United Airlines. From booking ☎️+1(888) 642-5075 to documentation and in-flight requirements, this hotline gives you access to the most reliable and current policies. ☎️+1(888) 642-5075 ensures you speak with someone trained to handle pet-specific travel needs, whether you're flying domestically or internationally. Don’t rely on outdated web articles or assumptions—every route, breed, and situation can have different rules. This number will help you avoid costly mistakes, delays, and denied boarding. Pet owners know that flying with an animal is a serious responsibility, and it requires precise coordination. By using the correct phone line, you gain confidence, peace of mind, and expert assistance. Save this number to your contacts and call well ahead of your trip. The earlier you begin planning, the smoother the journey will be for you and your pet. Whether you're taking a cat in the cabin or shipping a large dog through PetSafe, make ☎️+1(888) 642-5075 your first step.

The Stanford Dogs dataset contains 20,580 images of 120 classes of dogs from around the world, which are divided into 12,000 images for training and 8,580 images for testing.

Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.

Methods We systematically searched for any publication using the following search terms in Google Scholar and ISI Web of Knowledge: wildlife detect* dog, species detect* dog, scat detect* dog, [species] + detect* dog, [author] + detect* dog, [country] + detect* dog, conservation (detect*) dog, predator (detect*) dog, protected species (detect*) dog, den detect* dog, roost detect* dog, plant detect* dog, canine detection, and tracking dog. We traced any potentially relevant cited publication and only included those in our review that we could check ourselves. We also collected publications if we got to know them otherwise and reviewed existing literature lists and compilations (Grimm-Seyfarth et al. 2021, Appendix S1.1). We focused mainly on scientific literature, including scientific papers, dissertations, and project reports. However, wildlife detection dogs were frequently used for conservation or management purposes without a scientific research project behind them. For a more comprehensive overview of their deployment and performance, we included popular science or newspaper articles when no scientific publication about the project was found. In addition, we used social media platforms to obtain many articles from different countries (Grimm-Seyfarth et al. 2021, Appendix S1.1). In order to avoid multiple citations of the same study for which publications from different sources have been published, we compared each new entry with the entries in the database and preferably included scientific publications, followed by books, popular science and newspaper articles.

We compiled the data in a relational database (Microsoft Access 2013) consisting of five basic tables: literature, dog breeds, target species, target types and countries. We classified dog breeds into the ten FCI classification groups and breeds not listed as “not classified”. We assigned mixed breeds to a main or first-mentioned breed or to the category “Mix” when they could not be assigned to a specific breed. We classified target species according to their Latin and English names, genus, family, order, class, phylum and kingdom, adding subspecies names if provided. If the dog detected species groups without further specification (e.g., bat or bird carcasses, rodents, weed), we retained this group only. Taxonomic changes due to splitting of taxa into several species were only made if the allocation to the new species was obvious from the geographic information provided or had already been done by other authors. We divided potential target types into: living or dead individuals; nests, dens, clutches, coveys, roosts; scat, urine, saliva, glandular secretion; spores, eggs; larvae; hair, feathers, pellets, shed skin; and different combinations thereof. Lastly, we classified countries according to the (sub-) continent into North, Central and South America, Europe, Asia, Africa, and Oceania, assigning Russia and Turkey to “Eurasia”. Furthermore, we assigned Australia, New Zealand, and all oceanic islands (including subantarctic islands) to “Oceania” and made no differentiation to Zealandia.

In a main table, we then assigned each breed-target species-country association per reference as a single “case”. We marked pure-breed dogs and added a second breed for mixed breeds (if provided), as well as the number of dogs per breed and reference (if not mentioned directly, “1” for mentioning “dog” and “2” for mentioning “dogs”). We also added specifications to the country (e.g. Islands). If available, we extracted results of the wildlife detection dog performance compared to other monitoring methods. We classified the performance into four categories: dogs were (i) better; (ii) equal; or (iii) worse than other methods tested; or (iv) mixed results. The factor in comparison was study-specific and could include speed per area or transect, area size, sample size, quality, detectability, specificity, sensitivity, accuracy, or precision. We relied on those conservative measures since different monitoring methods can hardly be compared otherwise. The category “mixed results” was given when the dogs were better at some factors but worse at others, or when the performance depended upon season, year, site, or dog. Since we designed the database as a relational database, IDs among the five basic tables and the main table were linked together for quick searches and queries.

Explaining and predicting animal migration under global change This README file was generated on 10th November 2023 by Christine Howard ## GENERAL INFORMATION 1. Title of the Dataset: Explaining and predicting animal migration under global change 2. Author Information A. Principal Investigator Contact Information Names: Steve G. Willis Institution: Durham University Address: Department of Biosciences, Durham University, South Road, Durham, UK, DH1 3LE Email: s.g.willis@durham.ac.uk B. Associate or Co-Investigator Contact Information Names: Christine Howard Institution: Durham University Address: Department of Biosciences, Durham University, South Road, Durham, UK, DH1 3LE Email: christine.howard@durham.ac.uk 3. Date of data collection (single date, range, approximate date): 2012-2016 4. Geographic location of data collection: Europe and Africa 5. Information about funding sources that supported the collection of the data: Natural Environment Research Council, Award: NE/T001038/1, Swiss National Science Foundation, Award: SNF 31BD30_184120, Belgian Federal Science Policy Office, Award: BelSPO BR/185/A1/GloBAM-BE, Dutch Research Council, Award: NWO E10008, Academy of Finland, Award: 326315, National Science Foundation, Award: NSF 1927743 ## SHARING/ACCESS INFORMATION 1. Licenses/ restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license 2. Links to publications that cite or use the data: Howard, C., Mason, T.H.E., Baillie, S., Border, J., Hewson, C., Houston, A., Pearce-Higgins, J., Bauer, S., Willis, S., Stephens, S., (2023) Explaining and predicting animal migration under global change, Diversity and Distributions 1. Links to other publicly accessible locations of the data: None 2. Links/ relationships to ancillary data sets: None 3. Was data derived from another source? Yes A. If yes, list source (s): https://www.ospo.noaa.gov/Products/land/gvi/. 4. Recommended citation for this dataset: Howard, C., Mason, T.H.E., Baillie, S., Border, J., Hewson, C., Houston, A., Pearce-Higgins, J., Bauer, S., Willis, S., Stephens, S., (2023). Explaining and predicting animal migration under global change [Dataset]. Dryad. https://doi.org/10.5061/dryad.wdbrv15tn ## DATA & FILE OVERVIEW 1. File List: A) NDVI NOAA STAR 500km mean 2012 - 2016 EQA.csv 2. Relationship between files, if important: None 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA ## DATA - SPECIFIC INFORMATION FOR: NDVI NOAA STAR 500km mean 2012 - 2016 EQA.csv 1. Number of variables: 370 2. Number of cases/rows: 274 3. Variable List: * x: Longitude at centre of 500km hexagonal grid cell * y: Latitude at centre of 500km hexagonal grid cell * X: Data indexing code (relict) * id: Cell identification code * land: Land classification (1= land, 0 = sea) * X1 - X365: Mean 2012- 2016 interpolated daily NDVI for the hexagonal grid cell (X1 = 1st January, X365 = 31st December) 4. Missing data codes: NA 5. Specialised formats or other abbreviations used: NA Many migratory species are declining due to global environmental change. Yet, their complex annual cycles make unravelling the impacts of potential drivers such as climate and land-use change on migrations a major challenge. Identifying where, when, and how threatening processes impact species’ migratory journeys and population dynamics is crucial for identifying effective conservation actions. Here, we describe how a new migration modelling framework – Spatially-explicit Adaptive Migration Models (SAMMs) – can simulate the optimal behavioural decisions required to migrate across open land- or seascapes varying in character over space and time, without requiring predefined behavioural rules. Models of adaptive behaviour have been used widely in theoretical ecology but have great untapped potential in real-world contexts. Applying adaptive behaviour models across open environments will allow users to explore flexibility in how migratory strategies respond to environmental change and the consequences of migrants not being able to adapt to change. We outline how SAMMs can be used to model migratory journeys through aerial, terrestrial, and aquatic environments, demonstrating their potential using a case study on the common cuckoo (Cuculus canorus) and comparing modelled to observed behaviours. SAMMs offer a tool to identify the key threats faced by migratory species and to predict how they will adapt to future migratory journeys in response to changing environmental conditions. Example code for running Spatially-explicit Adaptive Migration Models (SAMMs) is pr...

This dataset contains the most up to date version of GLW 4 for the reference year 2020 for the following species: buffalo, cattle, sheep, goats, pigs and chicken. The individual species datasets are available at global extent and 5 minutes of arc resolution (approx. 10 km at the equator).

The fourth version of GLW, compared to the previous ones, reflects the most recently compiled and harmonized subnational livestock distribution data and much more detailed metadata.

The layers contain the density of animals per km², with weight estimated by the Random Forest model. The livestock species modelled include: buffaloes, cattle, chickens, goats, pigs and sheep.

All datasets are licensed through a Creative Commons Attribution 4.0 International License.

References

• Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010

• Income Disparities and the Global Distribution of Intensively Farmed Chicken and Pigs

• Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Data publication: 2024-07-15

Supplemental Information:

Unit: head/pixel or birds/pixel

Data type: Float64

No data value: No data

Spatial resolution: Approximately 10km (0.08333 degrees)

Spatial extent: World

Spatial Reference System (SRS): EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Contact points:

Resource Contact: Dominik Wisser (FAO-NSAL)

Metadata Contact: Giuseppina Cinardi (FAO-NSAL)

Data lineage:

Recommentations on data representation

The standard lat/long visualisation of the global raster datasets tends to visually over-represent animal densities in pixels located in northern latitudes as they cover a much lower surface on earth than those close to the equator. Thus, altough the data files are distributed in lat/long, we recommend the use of an equal-area projection for a proper representation of densities of our livestock data.

Resource constraints:

Public-use data under the CC BY-NC-SA 3.0 IGO license.

Online resources:

Buffalo: metadata

Chicken: metadata

Cattle: metadata

Goats: metadata

Pigs: metadata

Sheep: metadata

Data for download: All species density

Data for download: Buffalo density

Data for download: Chicken density

Data for download: Cattle density

Data for download: Goats density

Data for download: Pigs density

Data for download: Sheep density

This dataset contains the global distribution of pigs in 2010 expressed in total number of pigs per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3). Please go through the 1_Pg_2010_Metadata.html file for more information about this dataset and the set of included files.

Context

Data downloaded from the EMPRES Global Animal Disease Information System.

Content

Data shows the when, where and what of animal disease outbreaks from the last 2 years, including African swine fever, Foot and mouth disease and bird-flu. Numbers of cases, deaths, etc are also included.

Acknowledgements

This data is from the Food and Agriculture Organization of the United Nations. The EMPRES-i system can be access here Read more about the details of the system here

Each heading below describes a column of the travel speed dataset.

move_speed_ref: Unique reference number for each study reporting animal travel speed. The corresponding references are contained within the supplementary reference list file.

move_mass_ref: Unique reference number for each study reporting animal body mass. The corresponding references are contained within the supplementary reference list file.

scientific_name: Scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2022). A small number of studies that report only the genus name or common name of the species are reported in our dataset as Genus sp. (e.g. Gazella sp.). One study, which estimated the travel speed and body mass of an unknown species of mouse via camera traps, is reported in our dataset as Amazon mouse sp.

move_movement_mode: Categorical value indicating whether the reported travel speed corresponds to an animal engaged in flying, running, or swimming. This allows species to be accommodated that are capable of multi-modal locomotion (e.g. northern elephant seal, Mirounga angustirostris).

taxon_group: Categorical value indicating the membership to one of eight animal groups (amphibian, arthropod, cnidarian, bird, fish, mammal, mollusc, reptile).

thermo_reg: Categorical value indicating thermoregulatory strategy, i.e. the contribution of metabolic heat production to the maintenance of core body temperature ( during rest: Ectotherm (negligible contribution: matches ambient temperature), mesotherm (weak contribution: thermal lability in such as in tunas, lamnid sharks and leatherback sea turtles), endotherm (strong contribution: metabolic stable even when ambient temperatures are significantly below ).

move_medium: Categorical value indicating whether the animal’s travel speed was measured during locomotion within the terrestrial realm (air) or aquatic realm (water).

move_speed_method: Categorical value indicating whether travel speed was estimated directly (i.e. instantaneously via direct observation in real-time, animal-attached speedometer, or video recording) or indirectly from higher-resolution telemetry data (i.e. from changes in an animal’s spatial coordinates at intervals < 30 minutes apart).

move_study_condition: Categorical value indicating whether travel speed was estimated under natural field conditions or under controlled laboratory setting such within an aquarium or mesocosm. Travel speeds of the smallest animals (e.g. arthropods) can only feasibly be estimated within a controlled setting.

move_avgspeed_value: Categorical value indicating whether average travel speed was reported as a mean or median within the study.

move_bodymass_kg: Continuous value indicating the average adult body mass (units: kilograms) of the species. In cases where the study’s reference numbers move_speed_ref and move_mass_ref differ, we referred to secondary literature sources to assign the average adult body mass of the species. In cases where only body length was given, we used published allometric equations to estimate the wet body mass

move_avgspeed_ms: Continuous value indicating the average travel speed (units: metres per second) of the species reported within the study.

As a source of animal and plant population data, The Global Population Dynamics Database is unrivalled. Nearly five thousand separate time series are available here. In addition to all the population counts, there are taxonomic details of over 1400 species. The type of data contained in the GPDD varies enormously, from annual counts of mammals or birds at individual sampling sites, to weekly counts of zooplankton and other marine fauna. The project commenced in October 1994, following discussions on ways in which the collaborating partners could make a practical and enduring contribution to research into population dynamics. A small team was assembled and, with assistance and advice from numerous interested parties we decided to construct the database using the popular Microsoft Access platform. After an initial design phase, the major task has been that of locating, extracting, entering and validating the data in all the various tables. Now, nearly 5000 individual datasets have been entered onto the GPDD. The Global Population Dynamics Database comprises six Tables of data and information. The tables are linked to each other as shown in the diagram shown at http://cpbnts1.bio.ic.ac.uk/gpdd/Structur.htm. Referential integrity is maintained through record ID numbers which are held, along with other information in the Main Table. It's structure obeys all the rules of a standard relational database.